OpenRaiser/Intern-Atlas huggingface dataset:
AP: Selective Activation for De-sparsifying Pruned Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

The rectified linear unit (ReLU) is a highly successful activation function in neural networks as it allows networks to easily obtain sparse representations, which reduces overfitting in overparameterized networks. However, in network pruning, we find that the sparsity introduced by ReLU, which we quantify by a term called dynamic dead neuron rate (DNR), is not beneficial for the pruned network. Interestingly, the more the network is pruned, the smaller the dynamic DNR becomes during optimization. This motivates us to propose a method to explicitly reduce the dynamic DNR for the pruned network, i.e., de-sparsify the network. We refer to our method as Activating-while-Pruning (AP). We note that AP does not function as a stand-alone method, as it does not evaluate the importance of weights. Instead, it works in tandem with existing pruning methods and aims to improve their performance by selective activation of nodes to reduce the dynamic DNR. We conduct extensive experiments using popular networks (e.g., ResNet, VGG) via two classical and three state-of-the-art pruning methods. The experimental results on public datasets (e.g., CIFAR-10/100) suggest that AP works well with existing pruning methods and improves the performance by 3% - 4%. For larger scale datasets (e.g., ImageNet) and state-of-the-art networks (e.g., vision transformer), we observe an improvement of 2% - 3% with AP as opposed to without. Lastly, we conduct an ablation study to examine the effectiveness of the components comprising AP.
"Hey, Can You Add Captions?": The Critical Infrastructuring Practices of Neurodiverse People on TikTok
Venue: arXiv cs.HC 2022
Year: 2022

Accessibility efforts, how we can make the world usable and useful to as many people as possible, have explicitly focused on how we can support and allow for the autonomy and independence of people with disabilities, neurotypes, chronic conditions, and older adults. Despite these efforts, not all technology is designed or implemented to support everyone's needs. Recently, a community-organized push by creators and general users of TikTok urged the platform to add accessibility features, such as closed captioning to user-generated content, allowing more people to use the platform with greater ease. Our work focuses on an understudied population -- people with ADHD and those who experience similar challenges -- exploring the creative practices people from this community engage in, focusing on the kinds of accessibility they create through their creative work. Through an interview study exploring the experiences of creatives on TikTok, we find that creatives engage in critical infrastructuring -- a process of bottom-up (re)design -- to make the platform more accessible despite the challenges the platform presents to them as creators. We present these critical infrastructuring practices through the themes of: creating and augmenting video editing infrastructures and creating and augmenting video captioning infrastructures. We reflect on the introduction of a top-down infrastructure - the implementation of an auto-captioning feature - shifts the critical infrastructure practices of content creators. Through their infrastructuring, creatives revised sociotechnical capabilities of TikTok to support their own needs as well as the broader needs of the TikTok community. We discuss how the routine of infrastructuring accessibility is actually best conceptualized as incidental care work. We further highlight how accessibility is an evolving sociotechnical construct, and forward the concept of contextual accessibility.
Estimating Geographic Spillover Effects of COVID-19 Policies From Large-Scale Mobility Networks
Venue: arXiv cs.CY 2022
Year: 2022

Many policies in the US are determined locally, e.g., at the county-level. Local policy regimes provide flexibility between regions, but may become less effective in the presence of geographic spillovers, where populations circumvent local restrictions by traveling to less restricted regions nearby. Due to the endogenous nature of policymaking, there have been few opportunities to reliably estimate causal spillover effects or evaluate their impact on local policies. In this work, we identify a novel setting and develop a suitable methodology that allow us to make unconfounded estimates of spillover effects of local policies. Focusing on California's Blueprint for a Safer Economy, we leverage how county-level mobility restrictions were deterministically set by public COVID-19 severity statistics, enabling a regression discontinuity design framework to estimate spillovers between counties. We estimate these effects using a mobility network with billions of timestamped edges and find significant spillover movement, with larger effects in retail, eating places, and gyms. Contrasting local and global policy regimes, our spillover estimates suggest that county-level restrictions are only 54% as effective as statewide restrictions at reducing mobility. However, an intermediate strategy of macro-county restrictions -- where we optimize county partitions by solving a minimum k-cut problem on a graph weighted by our spillover estimates -- can recover over 90% of statewide mobility reductions, while maintaining substantial flexibility between counties.
ALP: Alleviating CPU-Memory Data Movement Overheads in Memory-Centric Systems
Venue: arXiv cs.AR 2022
Year: 2022

Partitioning applications between NDP and host CPU cores causes inter-segment data movement overhead, which is caused by moving data generated from one segment (e.g., instructions, functions) and used in consecutive segments. Prior works take two approaches to this problem. The first class of works maps segments to NDP or host cores based on the properties of each segment, neglecting the inter-segment data movement overhead. The second class of works partitions applications based on the overall memory bandwidth saving of each segment, and does not offload each segment to the best-fitting core if they incur high inter-segment data movement. We show that 1) mapping each segment to its best-fitting core ideally can provide substantial benefits, and 2) the inter-segment data movement reduces this benefit significantly. To this end, we introduce ALP, a new programmer-transparent technique to leverage the performance benefits of NDP by alleviating the inter-segment data movement overhead between host and memory and enabling efficient partitioning of applications. ALP alleviates the inter-segment data movement overhead by proactively and accurately transferring the required data between the segments. This is based on the key observation that the instructions that generate the inter-segment data stay the same across different executions of a program on different inputs. ALP uses a compiler pass to identify these instructions and uses specialized hardware to transfer data between the host and NDP cores at runtime. ALP efficiently maps application segments to either host or NDP considering 1) the properties of each segment, 2) the inter-segment data movement overhead, and 3) whether this overhead can be alleviated in a timely manner. We evaluate ALP across a wide range of workloads and show on average 54.3% and 45.4% speedup compared to only-host CPU or only-NDP executions, respectively.
Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits
Venue: arXiv cs.AI 2022
Year: 2022

The identification of addiction-related circuits is critical for explaining addiction processes and developing addiction treatments. And models of functional addiction circuits developed from functional imaging are an effective tool for discovering and verifying addiction circuits. However, analyzing functional imaging data of addiction and detecting functional addiction circuits still have challenges. We have developed a data-driven and end-to-end generative artificial intelligence(AI) framework to address these difficulties. The framework integrates dynamic brain network modeling and novel network architecture networks architecture, including temporal graph Transformer and contrastive learning modules. A complete workflow is formed by our generative AI framework: the functional imaging data, from neurobiological experiments, and computational modeling, to end-to-end neural networks, is transformed into dynamic nicotine addiction-related circuits. It enables the detection of addiction-related brain circuits with dynamic properties and reveals the underlying mechanisms of addiction.
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data
Venue: arXiv cs.LG 2022
Year: 2022

Sepsis is a deadly condition affecting many patients in the hospital. Recent studies have shown that patients diagnosed with sepsis have significant mortality and morbidity, resulting from the body's dysfunctional host response to infection. Clinicians often rely on the use of Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), and the Modified Early Warning Score (MEWS) to identify early signs of clinical deterioration requiring further work-up and treatment. However, many of these tools are manually computed and were not designed for automated computation. There have been different methods used for developing sepsis onset models, but many of these models must be trained on a sufficient number of patient observations in order to form accurate sepsis predictions. Additionally, the accurate annotation of patients with sepsis is a major ongoing challenge. In this paper, we propose the use of Active Learning Recurrent Neural Networks (ALRts) for short temporal horizons to improve the prediction of irregularly sampled temporal events such as sepsis. We show that an active learning RNN model trained on limited data can form robust sepsis predictions comparable to models using the entire training dataset.
Zero Knowledge Identification and Verification of Voting Systems
Venue: arXiv cs.CR 2022
Year: 2022

Current methods of voter identification, especially in India, are highly primitive and error-prone, depending on verification by (mostly) sight, by highly trusted election officials. This paper attempts to provide a trustless and zero-knowledge method of voter identification, while simultaneously reducing error. It also proposes a method for vote verification, that is, ensuring that the vote cast by a legal voter is registered as cast and tallied as registered. While numerous methods of zero-knowledge identification are available in the literature, very few of those are implementable on a large scale and subject to the type of constraints that are present, eg., in India. This paper attempts to provide a solution which, while preserving the integrity of the available methods, will also be more scalable and cost-effective.
Efficient Rate-Splitting Multiple Access for the Internet of Vehicles: Federated Edge Learning and Latency Minimization
Venue: arXiv cs.NI 2022
Year: 2022

Rate-Splitting Multiple Access (RSMA) has recently found favour in the multi-antenna-aided wireless downlink, as a benefit of relaxing the accuracy of Channel State Information at the Transmitter (CSIT), while in achieving high spectral efficiency and providing security guarantees. These benefits are particularly important in high-velocity vehicular platoons since their high Doppler affects the estimation accuracy of the CSIT. To tackle this challenge, we propose an RSMA-based Internet of Vehicles (IoV) solution that jointly considers platoon control and FEderated Edge Learning (FEEL) in the downlink. Specifically, the proposed framework is designed for transmitting the unicast control messages within the IoV platoon, as well as for privacy-preserving FEEL-aided downlink Non-Orthogonal Unicasting and Multicasting (NOUM). Given this sophisticated framework, a multi-objective optimization problem is formulated to minimize both the latency of the FEEL downlink and the deviation of the vehicles within the platoon. To efficiently solve this problem, a Block Coordinate Descent (BCD) framework is developed for decoupling the main multi-objective problem into two sub-problems. Then, for solving these non-convex sub-problems, a Successive Convex Approximation (SCA) and Model Predictive Control (MPC) method is developed for solving the FEEL-based downlink problem and platoon control problem, respectively. Our simulation results show that the proposed RSMA-based IoV system outperforms the conventional systems.
Graph Convolutional Networks for Traffic Forecasting with Missing Values
Venue: arXiv cs.LG 2022
Year: 2022

Traffic forecasting has attracted widespread attention recently. In reality, traffic data usually contains missing values due to sensor or communication errors. The Spatio-temporal feature in traffic data brings more challenges for processing such missing values, for which the classic techniques (e.g., data imputations) are limited: 1) in temporal axis, the values can be randomly or consecutively missing; 2) in spatial axis, the missing values can happen on one single sensor or on multiple sensors simultaneously. Recent models powered by Graph Neural Networks achieved satisfying performance on traffic forecasting tasks. However, few of them are applicable to such a complex missing-value context. To this end, we propose GCN-M, a Graph Convolutional Network model with the ability to handle the complex missing values in the Spatio-temporal context. Particularly, we jointly model the missing value processing and traffic forecasting tasks, considering both local Spatio-temporal features and global historical patterns in an attention-based memory network. We propose as well a dynamic graph learning module based on the learned local-global features. The experimental results on real-life datasets show the reliability of our proposed method.
Edge Computing for Semantic Communication Enabled Metaverse: An Incentive Mechanism Design
Venue: arXiv cs.GT 2022
Year: 2022

Semantic communication (SemCom) and edge computing are two disruptive solutions to address emerging requirements of huge data communication, bandwidth efficiency and low latency data processing in Metaverse. However, edge computing resources are often provided by computing service providers and thus it is essential to design appealingly incentive mechanisms for the provision of limited resources. Deep learning (DL)- based auction has recently proposed as an incentive mechanism that maximizes the revenue while holding important economic properties, i.e., individual rationality and incentive compatibility. Therefore, in this work, we introduce the design of the DLbased auction for the computing resource allocation in SemComenabled Metaverse. First, we briefly introduce the fundamentals and challenges of Metaverse. Second, we present the preliminaries of SemCom and edge computing. Third, we review various incentive mechanisms for edge computing resource trading. Fourth, we present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse. Simulation results demonstrate that the DL-based auction improves the revenue while nearly satisfying the individual rationality and incentive compatibility constraints.
Active Vibration Fluidization for Granular Jamming Grippers
Venue: arXiv cs.RO 2022
Year: 2022

Granular jamming has recently become popular in soft robotics with widespread applications including industrial gripping, surgical robotics and haptics. Previous work has investigated the use of various techniques that exploit the nature of granular physics to improve jamming performance, however this is generally underrepresented in the literature compared to its potential impact. We present the first research that exploits vibration-based fluidisation actively (e.g., during a grip) to elicit bespoke performance from granular jamming grippers. We augment a conventional universal gripper with a computer-controllled audio exciter, which is attached to the gripper via a 3D printed mount, and build an automated test rig to allow large-scale data collection to explore the effects of active vibration. We show that vibration in soft jamming grippers can improve holding strength. In a series of studies, we show that frequency and amplitude of the waveforms are key determinants to performance, and that jamming performance is also dependent on temporal properties of the induced waveform. We hope to encourage further study focused on active vibrational control of jamming in soft robotics to improve performance and increase diversity of potential applications.
Interactive Process Identification and Selection from SAP ERP
Venue: arXiv cs.DB 2022
Year: 2022

SAP ERP is one of the most popular information systems supporting various organizational processes, e.g., O2C and P2P. However, the amount of processes and data contained in SAP ERP is enormous. Thus, the identification of the processes that are contained in a specific SAP instance, and the creation of a list of related tables is a significant challenge. Eventually, one needs to extract an event log for process mining purposes from SAP ERP. This demo paper shows the tool Interactive SAP Explorer that tackles the process identification and selection problem by encoding the relational structure of SAP ERP in a labeled property graph. Our approach allows asking complex process-related queries along with advanced representations of the relational structure.
Body Segmentation Using Multi-task Learning
Venue: arXiv cs.CV 2022
Year: 2022

Body segmentation is an important step in many computer vision problems involving human images and one of the key components that affects the performance of all downstream tasks. Several prior works have approached this problem using a multi-task model that exploits correlations between different tasks to improve segmentation performance. Based on the success of such solutions, we present in this paper a novel multi-task model for human segmentation/parsing that involves three tasks, i.e., (i) keypoint-based skeleton estimation, (ii) dense pose prediction, and (iii) human-body segmentation. The main idea behind the proposed Segmentation--Pose--DensePose model (or SPD for short) is to learn a better segmentation model by sharing knowledge across different, yet related tasks. SPD is based on a shared deep neural network backbone that branches off into three task-specific model heads and is learned using a multi-task optimization objective. The performance of the model is analysed through rigorous experiments on the LIP and ATR datasets and in comparison to a recent (state-of-the-art) multi-task body-segmentation model. Comprehensive ablation studies are also presented. Our experimental results show that the proposed multi-task (segmentation) model is highly competitive and that the introduction of additional tasks contributes towards a higher overall segmentation performance.
Trajectory Privacy Protection Mechanism based on Social Attributes
Venue: arXiv cs.CR 2022
Year: 2022

The current trajectory privacy protection technology only considers the temporal and spatial attributes of trajectory data, but ignores the social attributes. However, there is an intrinsic relationship between social attributes and human activity trajectories, which brings new challenges to trajectory privacy protection, making existing trajectory privacy protection technologies unable to resist trajectory privacy attacks based on social attributes. To this end, this paper first studies the social privacy attack in the trajectory data, builds a social privacy attack model based on the fusion of "space-time" features, and reveals the internal impact of the spatial and temporal features in the trajectory data on social privacy leaks. -Anonymous algorithm and trajectory release privacy protection provide theoretical support. On this basis, integrate social attributes into trajectory privacy protection technology, design trajectory k-anonymity algorithm based on "space-time-social" three-dimensional mobile model, and construct trajectory data based on "space-time-social-semantic" multi-dimensional correlation Publish privacy-preserving models.
A fast and Accurate Sketch Method for Estimating User Similarities over Trajectory Data
Venue: arXiv cs.CR 2022
Year: 2022

In a complex urban environment, due to the unavoidable interruption of GNSS positioning signals and the accumulation of errors during vehicle driving, the collected vehicle trajectory data is likely to be inaccurate and incomplete. A weighted trajectory reconstruction algorithm based on a bidirectional RNN deep network is proposed. GNSS/OBD trajectory acquisition equipment is used to collect vehicle trajectory information, and multi-source data fusion is used to realize bidirectional weighted trajectory reconstruction. At the same time, the neural arithmetic logic unit (NALU) is introduced into the trajectory reconstruction model to strengthen the extrapolation ability of the deep network and ensure the accuracy of trajectory prediction, which can improve the robustness of the algorithm in trajectory reconstruction when dealing with complex urban road sections. The actual urban road section was selected for testing experiments, and a comparative analysis was carried out with existing methods. Through root mean square error (RMSE, root-mean-square error) and using Google Earth to visualize the reconstructed trajectory, the experimental results demonstrate the effectiveness and reliability of the proposed algorithm.
Categorical Tools for Natural Language Processing
Venue: arXiv cs.CL 2022
Year: 2022

This thesis develops the translation between category theory and computational linguistics as a foundation for natural language processing. The three chapters deal with syntax, semantics and pragmatics. First, string diagrams provide a unified model of syntactic structures in formal grammars. Second, functors compute semantics by turning diagrams into logical, tensor, neural or quantum computation. Third, the resulting functorial models can be composed to form games where equilibria are the solutions of language processing tasks. This framework is implemented as part of DisCoPy, the Python library for computing with string diagrams. We describe the correspondence between categorical, linguistic and computational structures, and demonstrate their applications in compositional natural language processing.
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree
Venue: arXiv cs.LG 2022
Year: 2022

Both clustering and outlier detection play an important role for meteorological measurements. We present the AWT algorithm, a clustering algorithm for time series data that also performs implicit outlier detection during the clustering. AWT integrates ideas of several well-known K-Means clustering algorithms. It chooses the number of clusters automatically based on a user-defined threshold parameter, and it can be used for heterogeneous meteorological input data as well as for data sets that exceed the available memory size. We apply AWT to crowd sourced 2-m temperature data with an hourly resolution from the city of Vienna to detect outliers and to investigate if the final clusters show general similarities and similarities with urban land-use characteristics. It is shown that both the outlier detection and the implicit mapping to land-use characteristic is possible with AWT which opens new possible fields of application, specifically in the rapidly evolving field of urban climate and urban weather.